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Influence of low spatial resolution a priori data on tropospheric NO2 satellite retrievals
Influence of low spatial resolution a priori data on tropospheric NO2 satellite retrievals
The retrieval of tropospheric columns of NO2 and other trace gases from satellite observations of backscattered solar radiation relies on the use of accurate a priori information. The spatial resolution of current space sensors is often significantly higher than that of the a priori datasets used, introducing uncertainties from spatial misrepresentation. In this study, the effect of spatial under-sampling of a priori data on the retrieval of NO2 columns was studied for a typical coastal area (around San Francisco). High-resolution (15 × 15 km2) NO2 a priori data from the WRF-Chem model in combination with high-resolution MODIS surface reflectance and aerosol data were used to investigate the uncertainty introduced by applying a priori data at typical global chemical transport model resolution. The results show that the relative uncertainties can be large (more than a factor of 2 if all a priori data used is at the coarsest resolution) for individual measurements, mainly due to spatial variations in NO2 profile and surface albedo, with smaller contributions from aerosols and surface height changes. Similar sensitivities are expected for other coastal regions and localised sources such as power plants, highlighting the need for high-resolution a priori data in quantitative analysis of the spatial patterns retrieved from satellite observations of tropospheric pollution.
- University of Colorado System United States
- University of Colorado Boulder United States
- Department of Geography United Kingdom
- Swansea University United Kingdom
- Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, CO, USA United States
4815
Beirle, S., Platt, U., Wenig, M., and Wagner, T.: Weekly cycle of NO2 by GOME measurements: a signature of anthropogenic sources, Atmos. Chem. Phys., 3, 2225-2232, doi:10.5194/acp-3- 2225-2003, 2003.
Beirle, S., Platt, U., von Glasow, R., Wenig, M., and Wagner, T.: Estimate of nitrogen oxide emissions from shipping by satellite remote sensing, Geophys. Res. Lett., 31, L18102, doi:10.1029/2004GL020312, 2004.
Bertram, T., Heckel, A., Richter, A., Burrows, J. P., and Cohen, R.: Satellite measurements of daily variations in soil NOx emissions, Geophys. Res. Lett., 32, L24812, doi:10.1029/2005GL024640, 2005.
Blond, N., Boersma, K. F., Eskes, H. J., van der A, R. J., Van Roozendael, M., De Smedt, I., Bergametti, G., and Vautard, R.: Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over western Europe, J. Geophys. Res., 112, D10311, doi:10.1029/2006JD007277, 2007.
Boersma, K. F., Eskes, H. J., and Brinksma, E. J.: Error Analysis for Tropospheric NO2 Retrieval from Space, J. Geophys. Res., 109, D04311, doi:10.1029/2003JD003962, 2004.
Boersma, K. F., Eskes, H. J., Veefkind, J. P., Brinksma, E. J., van der A, R. J., S neep, M., van den Oord, G. H. J., Levelt, P. F., Stammes, P., Gleason, J. F., and Bucsela, E. J.: Near-real time retrieval of tropospheric NO2 from OMI, Atmos. Chem. Phys., 7, 2103-2118, doi:10.5194/acp-7-2103-2007, 2007.
Boersma, K. F., Jacob, D. J., Eskes, H. J., Pinder, R. W., Wang, J., and van der A, R. J.: Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: Observing the diurnal evolution of chemistry and emissions from space, J. Geophys. Res., 113, D16S26, doi:10.1029/2007JD008838, 2008.
Boersma, K. F., Jacob, D. J., Trainic, M., Rudich, Y., DeSmedt, I., Dirksen, R., and Eskes, H. J.: Validation of urban NO2 concentrations and their diurnal and seasonal variations observed from the SCIAMACHY and OMI sensors using in situ surface measurements in Israeli cities, Atmos. Chem. Phys., 9, 3867-3879, doi:10.5194/acp-9-3867-2009, 2009.
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noel, S., Rozanov, V. V., Chance, K. V., and Goede, A.: SCIAMACHY: Mission objectives and measurement modes, J. Atmos. Sci., 56, 127-150, 1999.
Burrows, J., Weber, M., Buchwitz, M., Ladsta¨tter-Weißenmayer, A., Richter, A., DeBeek, R., Hoogen, R., Bramstedt, K., Eichmann, K.-U., Eisinger, M., and Perner, D.: The global ozone monitoring experiment (GOME): Mission concept and first scientific results, J. Atmos. Sci., 56, 151-175, 1999.
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Citations provided by BIP!Popularity provided by BIP!- University of Colorado System United States
- University of Colorado Boulder United States
- Department of Geography United Kingdom
- Swansea University United Kingdom
- Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, CO, USA United States
- University of Bremen Germany
- Cooperative Institute for Research in Environmental Sciences United States
- NOAA Earth System Research Laboratory United States
The retrieval of tropospheric columns of NO2 and other trace gases from satellite observations of backscattered solar radiation relies on the use of accurate a priori information. The spatial resolution of current space sensors is often significantly higher than that of the a priori datasets used, introducing uncertainties from spatial misrepresentation. In this study, the effect of spatial under-sampling of a priori data on the retrieval of NO2 columns was studied for a typical coastal area (around San Francisco). High-resolution (15 × 15 km2) NO2 a priori data from the WRF-Chem model in combination with high-resolution MODIS surface reflectance and aerosol data were used to investigate the uncertainty introduced by applying a priori data at typical global chemical transport model resolution. The results show that the relative uncertainties can be large (more than a factor of 2 if all a priori data used is at the coarsest resolution) for individual measurements, mainly due to spatial variations in NO2 profile and surface albedo, with smaller contributions from aerosols and surface height changes. Similar sensitivities are expected for other coastal regions and localised sources such as power plants, highlighting the need for high-resolution a priori data in quantitative analysis of the spatial patterns retrieved from satellite observations of tropospheric pollution.